The presence of undesirable entities in textile materials such as neps and trash particles is a problem whose severity is generally increasing. Production and harvesting techniques of cotton, for example, demand more aggressive cleaning action at the gin or in the early stages of processing in the textile mill. These actions remove foreign matter or trash but in many cases break the trash into smaller particles and leave some of it in the fibrous mass. This makes it more difficult to remove in later stages. Worse, this increasingly aggressive cleaning action generally increases the level of nep formation. It is therefore increasingly important to monitor the levels of these undesirable entities on a continuous basis in the gin or mill in order to optimally control them; one must measure before one can control.
In most production environments it is completely impossible to monitor 100% of the process throughput and samples of in-process material must be acquired for measurement. In textile processing machines the fiber states available for sampling are in tuft form or in sliver. New means are therefore needed to acquire a representative sample and prepare it into thin web format for image analysis measurement. There are notable exceptions where judicious application of recently-developed image analysis technology enable 100% monitoring of the process throughput. A good example, as will be disclosed below in a preferred embodiment, is monitoring the thin web of a carding machine. Prior art methods and apparatus result in overwhelmingly expensive or otherwise impractical applications of image analysis. Our invention overcomes the difficulties.